Deep Loss Driven Multi-Scale Hashing Based on Pyramid Connected Network
Lingchen Gu, Ju Liu, Xiaoxi Liu, Jiande Sun · IEEE Transactions on Multimedia · 2020
Thanks to the great success of the deep learning, deep hashing for large-scale multimedia retrieval has made significant progress recently. However, most existing deep hashing algorithms suffer from slow convergence due to the gradient vanishing problem, caused by deep network structures and saturated activation functions. Moreover, a single convolution layer is often followed by down-sampling such as max pooling, resulting in local information loss that might affect the overall system robustness and performance. In this work, we propose a novel deep supervised hashing, Deep Loss Driven Multi-Scale Hashing (DLDMSH), which learns the high-quality approximate binary codes through an end-to-end network and improves the representative capacity of hash codes for large-scale image retrieval. Specifically, we design a Loss Driven Multi-Scale (LDMS) feature which is aggregated from convolutional feature maps. Moreover, a Pyramid Connected Convolutional Neural Network (PCNet) architecture is devised to generate LDMS feature, which inputs pairs of images during the training and outputs an image to approximate discrete values. In particular, 1 × 1 convolution kernels are applied to make a linear combination of features for realizing feature reduction, and the reduced features are fused in the fusion layer. This effectively improves the performance of deep features. A novel loss function preserving semantic information is integrated into an end-to-end learning scheme, which enhances the representative capacity of binary codes. Extensive experiments over four benchmark datasets show that DLDMSH significantly outperforms several other state-of-the-art hashing methods.